HI Kinsley, (01)
If we can go back to the issue that started this thread it was the
performance of OWL for massive quantities of data. This was Ed and John's
point and the one I agreed with. The extra point I made was that there is
always a combination of tools (or cocktail as you say) that go into the
building of a successful decision support system which John then laid out
what he does. (02)
So I think we agree here. (03)
KI>" Here is a much simpler solution. You point to some disparate data and
the also provide a link to your solution. That I can respond to much
easier, how about that?" (04)
I am not sure what this means?? Can you explain? What "solution" are you
referring to? If you mean our reasoning platform, it sits inside the secure
CDC Firewall and the data is secure and requires Federal Security clearance
to access. We do not market a public "product" per se. (05)
KI> " As for your processing off a stream, what point are you trying to make (06)
about how the data is going to be accessed, reconciled, and meshed?
Where does thinking occur? Where does remembering occurring? What's the
grey matter? How does the machine construct frames of references when
dealing with these huge streams of disparate data?" (07)
I am attaching a paper we presented at AAAI Regarding the "Intelligence" in
Distributed Intelligent Systems, Fall Symposium a couple of years ago that
gives you a high level view of what the system does. The Disease profile is
modeled in an ontology language using a particular message profile we expect
to see in the data stream. We express the triples in JESS facts and use a
service bus to maintain specific "semantic" listeners that know about a
particular kind of fact and interact with a knowledge controller that knows
how to integrate the listeners. (08)
As message are streamed through the message factory, identifiers are
correlated for a particular patient and new data is checked againt the
profile to see if a threshold criteria is met. (09)
I looked at the products you build on your site. Very impressive. Just not
what we use in our solutions at present. (010)
Cecil
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Kingsley Idehen
Sent: Friday, October 09, 2009 6:31 PM
To: [ontolog-forum]
Subject: Re: [ontolog-forum] [SPAM] Re: Ontology-based database integration (011)
Cecil O. Lynch, MD, MS wrote:
> HI Kingsley,
>
> I don't think you are getting the message here. There is no "RDBMS" in
> play. These messages are not dumped to a database then processed. They
> are processed in real-time, in memory, off the data stream. If you had
> time to database them, then you are not in need of real-time analysis.
> That is not the use case I am talking about here, or in most decision
> support that we do in healthcare.
Maybe I am not being clear, even though I explicitly mentioned a
combination of things.
My point is about using a cocktail of technologies to solve a problem,
and at no point in time have I implied that this is solely an RDBMS
game. Its a DBMS game. (012)
As for your processing off a stream, what point are you trying to make
about how the data is going to be accessed, reconciled, and meshed?
Where does thinking occur? Where does remembering occurring? What's the
grey matter? How does the machine construct frames of references when
dealing with these huge streams of disparate data? (013)
I am an insatiable learner, so please show me the way. (014)
>
> You need to process that data as soon as the submit button is hit from
> an application and the message is transmitted to the router.
> Processing that data means comparing the chief complaint, signs and
> symptoms, clinical observations, radiology tests and lab data of that
> patient against hundreds of other diseases that might mimic it,
> maintain in memory facts that may be of importance, orchestrate the
> timing relations of the data coming in so that you know a lab was
> drawn before a medication that might affect the lab result and not
> after, and modify your confidence as data aggregates on the fly,
> reporting your new facts to listeners on the service bus that may need
> those facts to correlate new data that it (the listener) has.
>
> The issue is that DL inference cannot scale to this nor does its
> monotonicity allow this type of reasoning.
>
> Also, you are talking about SPARQL endpoint that would be too limited
> to handle the OWL 2 constructs we use anyway, even if we could get the
> performance. In that case we would likely use SWRL.
No, the SPARQL endpoint (Virtuoso) doesn't implement OWL2 constructs
yet, that's all. That said, is all ready handles a far chunk of
pragmatic axioms from the prior release. (015)
Here is a much simpler solution. You point to some disparate data and
the also provide a link to your solution. That I can respond to much
easier, how about that? (016)
BTW - Our sponger middleware [1] might interest you, and don't assume
its simply about extracting and transforming data into RDF. It makes
de-referencable proxy/wrapper URIs which you can then use directly in
Virtuoso's SPARQL queries with inference rules (as per my prior post)
enabled. The HTML pages also contain RDFa. All of this is about
producing data conduction via HTTP URIs (applies to the Web, Intranets,
or Extranets). (017)
Links: (018)
1. http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VirtSponger
2. http://pnt.me/0WeX4O - processing a BestBuy page on the fly and
looking up other data sources on the Web (follow foaf:primarytopic or
foaf:topic ; then seeAslso)
3. http://pnt.me/yguwm3 -- example using Michael Jackson (ditto re.
links to follow, note Amazon, Guardian, OpenCalais, and lots of other
links in the graph generated) (019)
Kingsley
>
> Cecil
>
> Cecil O. Lynch, MD, MS
>
>
>
> -------- Original Message --------
> Subject: Re: [ontolog-forum] [SPAM] Re: Ontology-based database
> integration
> From: Kingsley Idehen <kidehen@xxxxxxxxxxxxxx>
> Date: Fri, October 09, 2009 3:16 pm
> To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
>
> Cecil O. Lynch, MD, MS wrote:
> > Hi Kingsley,
> >
> > I am talking about reasoning. The data sets are HL7 messages
> passed on
> > disease surveillance to the US CDC. The system that we built
> processes
> > an XML message, reasons against the syntax of the message to confirm
> > the structure, reasons over the data contained in the message,
> > to validate the terminology, then reasons against the content to
> > determine the disease status (drug resistance pattern, treatment
> > guideline adherence).
> Okay, lets take a look at Web Scale Reasoning.
>
> Scenario: Information about Michael Jackson
> Data Sources: various data spaces on the Web e.g., Last.FM,
> Discogs.org,
> MusicBrainz, DBpedia, blog posts from Live Journal etc..
> Virtuoso instance information: 7.5+ Billion Triples, SPARQL endpoint
> live on the Web at: http://lod.openlinksw.com
> <http://lod.openlinksw.com/>
>
> Actions:
> All of this data gets into the Virtuoso Quad store via a variety of
> means (remember, Virtuoso is an RDMS and Quad Store Hybrid)
> Inference Rules in play: explicit co-reference via "owl:sameAs"
> and much
> fuzzier indirect co-reference using a local axiom that designates
> "foaf:name" as being an Inverse Functional Property (IFP).
>
> Links:
>
> 1. http://bit.ly/38Jlw4 - Page showing the effect of explicit and
> fuzzier reasoning (note the obvious stick out errors re. entity
> Michael
> Jackson); here de-referencing each URI will expose the same expanded
> union of data from across all the data spaces (named graphs)
> 2. http://bit.ly/LKtnt - Page showing same data but without the
> inference rules context in play; here each URI will de-reference
> to its
> data space specific data (no union expansion)
> > All 50 states send messages to the system. When you start getting
> > into the complex reasoning for these types of medical messages
> for an
> > entire country, OWL does not scale.
> Really? It doesn't scale if forward-chaining is in play and
> unconstrained. I am demonstrating Web Scale reasoning using
> backward-chaining. Naturally, since some of the data gets into the
> Quad
> Store via our Sponger Middleware (an RDFizer middleware component),
> there is some constrained forward-chaining as part of the sponger
> processing pipeline.
>
> Conclusion:
>
> We shouldn't write-off anything, its about using the best
> combination of
> tools for the problem at hand. In this case, the trick is to combine
> technology and techniques from a range of realms: raw DBMS and
> Middleware.
>
>
> Kingsley
> >
> > Cecil O. Lynch, MD, MS
> >
> >
> >
> > -------- Original Message --------
> > Subject: [SPAM] Re: [ontolog-forum] Ontology-based database
> > integration
> > From: Kingsley Idehen <kidehen@xxxxxxxxxxxxxx>
> > Date: Fri, October 09, 2009 10:41 am
> > To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
> >
> > Cecil Lynch wrote:
> > > John,
> > >
> > > I would say that I completely agree with you that for large volume
> > > transaction based systems, OWL or RDF are hopeless from a
> performance
> > > perspective.
> > Cecil,
> >
> > What have you tested? How have you arrived at your conclusions?
> >
> > You are assuming that RDBMS and RDF Graph hybridization (all the way
> > down to the engine core) isn't achievable, right?
> >
> > What performance are we talking about? Instance data queries,
> > reasoning
> > over the ABox and TBox etc.?
> >
> > I really think that when we talk about data integration and the
> > prowess
> > of RDF, OWL, and what can be achieved re. reasoning etc.. Best we
> > point
> > to actual data available on the Web.
> >
> > Links:
> >
> > 1. http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/ --
> > SPARQL Benchmark with RDBMS to RDF component
> >
> > Kingsley
> > > That said, I think that OWL 2 is a great language for basing
> > > your model to ensure formalism in the development of the domain
> > of interest,
> > > but then you need to hand it off to another reasoning approach.
> > >
> > > There is no perfect ONE language, including Prolog, to approach
> > most real
> > > world, real time reasoning. In our experience, the best
> > performance and
> > > logic come from assembling a suite of tools that can work
> > together in an
> > > orchestrated services platform. There are some problems I want to
> > address
> > > using DL or regression analysis, others with classification and
> > still others
> > > with backward chaining. Some tools are better for each of these
> > and I think
> > > the best systems use them in concert.
> > >
> > > Cecil
> > >
> > > -----Original Message-----
> > > From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
> > > [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx
> <http://email.secureserver.net/#Compose>
> > <http://email.secureserver.net/#Compose>] On Behalf Of John F. Sowa
> > > Sent: Friday, October 09, 2009 12:27 AM
> > > To: edbark@xxxxxxxx
> > > Cc: [ontolog-forum]
> > > Subject: Re: [ontolog-forum] Ontology-based database integration
> > >
> > > Ed,
> > >
> > > I agree with most of your response to my remark, with the
exception
> > > of one sentence.
> > >
> > > JFS>> DL is just one of a large number of logic-based technologies
> > > >> that produce useful results for certain kinds of problems.
> > > >> Unfortunately, people are being forced to use OWL for tasks
> > > >> that it was never designed to do. They go through contortions
> > > >> that make Perl look like the epitome of structured elegance.
> > >
> > >
> > > EB> I fully agree. Part of that is the silver bullet mentality:
> > > > OWL is the best technology available; so whatever contortion you
> > > > have to perform to use it is the best you could have done. And
> > > > we are both familiar with the software engineer's pride of
> > > > accomplishment in building a Rube Goldberg device to solve a
> > > > problem that would be a simple application of a technology he
> > > > is unfamiliar with. But we have made progress -- it is not
> > > > a primitive AI application coded in Fortran anymore.
> > >
> > > The point that I very strongly disagree with is that "OWL is
> > > the best technology available." At VivoMind, we are delighted
> > > when our competitors use OWL because we can translate their
> > > sources to Prolog and get orders of magnitude improvement over
> > > their "native" implementations.
> > >
> > > But for heavy-duty lifting (gigabytes and terabytes) we would
> > > never dream of using RDF or OWL. Those languages are hopelessly
> > > inadequate for truly massive volumes of data. Just note that
> > > Google doesn't use those languages. They know better.
> > >
> > > John
> > >
> > >
> > > _________________________________________________________________
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> > --
> >
> >
> > Regards,
> >
> > Kingsley Idehen Weblog: http://www.openlinksw.com/blog/~kidehen
> <http://www.openlinksw.com/blog/%7Ekidehen>
> > <http://www.openlinksw.com/blog/%7Ekidehen>
> > President & CEO
> > OpenLink Software Web: http://www.openlinksw.com
> <http://www.openlinksw.com/>
> > <http://www.openlinksw.com/>
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> --
>
>
> Regards,
>
> Kingsley Idehen Weblog: http://www.openlinksw.com/blog/~kidehen
> <http://www.openlinksw.com/blog/%7Ekidehen>
> President & CEO
> OpenLink Software Web: http://www.openlinksw.com
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Regards, (022)
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